Information on the location and type of an unknown emitter is a valuable commodity that can be used for exploitation of the emitter's signals. In modern warfare, troop movements, deployments and activity level can often by tracked, identified and quantified by the wireless traffic, the type of emitters used and specific emitter used. Information regarding location of a signal source such as for surveillance or combat search and rescue can also be of great value in the case of a downed pilot or a marked terrorist under surveillance. Additionally, command and control among conventional and/or special operation forces can be illuminated through their wireless communication.
Automatic recognition of digital modulation formats is also increasingly important as the number and sophistication of digital signaling system increase. There is an emerging need for intelligent receivers capable of quickly discriminating signal types. Modulation classification may be used to identify interferences or to choose the appropriate demodulator in the cooperative scenario.
FIG. 1 shows the power spectral densities for Phase shift keying (PSK)/Quadrature Amplitude modulation (QAM) waveforms (Binary PSK, Quadrature PSK 8PSK, 16-QAM, 64 QAM and 256 QAM. As clearly seen in FIG. 1 the power spectral densities for each of these waveforms are nearly identical. Therefore, prior art system using spectral densities are limited in their ability to discriminate and thus classify these waveforms.
Intentional detection of the signal or message can be accomplished in military systems that use specially designed electronic support measures (ESM) receivers. These ESM receivers are often found in signal intelligence (SIGINT) applications. In commercial applications, devices employed by service providers (i.e. spectral monitors, error rate testers) can be used to detect intrusion on their spectral allocation
ESM/SIGINT systems often need to classify and/or characterize the waveforms of unknown source emitters within their field of view (FOV). Classification identifies the type of signal being emitted by the unknown emitter. Characterization is identifying the particularities of the signal that are a result of and stem from the actual emitter (specific emitter) used to transmit the signal. Characterization and classification and other parameters are collectedly referred to as waveform “fingerprinting”. Waveform fingerprinting can support and improve the abilities of SIGINT system to perform modulation recognition, identify friend or foe emitters, intercept transmitted messages and characterize transmitter imperfections for example local oscillator phase jitter, non-linearities in the power amplifier, etc.
Many current emitter classification/characterization methods are based on second-order statistics (correlation) and power spectra estimation. Spectrum estimation identifies the waveform spectrogram to obtain the time-frequency characteristics while ignoring phase information that can provide additional beneficial information. Consider the example where a deceptive transmitter alters the channel filter (i.e., the Nyquist pulse shaping) between maximum phase and minimum phase realizations. Using only prior art power spectrum (or other prior art second-order techniques) estimation and ignoring phase, this type of modulation is undetectable. However, using the present inventive techniques, this type of modulation, as well as many others, can be detected, estimated, and classified using fourth-order domain statistics.
There are other current emitter classification/characterization methods known in the prior art that are based on higher-order statistic or polyspectra analysis. But these methods use third-order cumulants and corresponding bi-spectrum, zero-lag 4th-order cumulants and model based methods using 4th-order cumulant slices through possible combinations of lag triplets. These prior art methods neglect higher-order statistical structure at different time lags and use specific cumulant formulations not necessarily well-suited to signal characterization and classification. The present inventive techniques disclosed herein differ from the prior art higher-order statistics approach in that, for the inventive techniques, the basis for waveform characterization and classification uses a unique 4th-order cumulant definition, a multiplicity of lag triplet choices, and a unique matrix pencil formulation to form a complex 3-D 4th-order cumulant matrix volume. The data in the cumulant volume is used in its entirety as the basis of discrimination. Further, this method is general and applicable to an extremely broad range of signal characterization and classification problems.
The present subject matter provides new statistical features, or “3-D fingerprints” for emitter waveform classification and characterization. The subject matter exploits the full multidimensional volume of higher-order cumulants' variations over different lag combinations rather than just single zero-lag cumulant values or cumulant slices along one non-zero lag as used in some prior art methods. The present subject matter augments existing methods for signal classification and is complementary to existing techniques using auto correlations, power spectra and spectrograms. Higher-order cumulants, as defined herein, enable the waveform analysis system to have access to waveform shape information that is typically unavailable when using prior art methods. Furthermore, the multidimensional cumulants defined by the present disclosure are insensitive to signal power.
It is an object of the disclosed subject matter to present a novel method for obtaining the “3-D fingerprint” of a received waveform. The method includes sampling the received waveform to obtain samples of an attribute of the received waveform; and estimating a multidimensional higher-order nonzero-lag auto-cumulant of the received waveform attribute samples to obtain the fingerprint of the received waveform.
It is also an object of the disclosure to present a novel method for classifying a received waveform. The method includes sampling the received waveform to obtain a predetermined number of samples of an attribute of the received waveform; estimating a multidimensional higher-order nonzero-lag auto-cumulant of the received waveform attribute samples; and comparing the estimated auto-cumulant of the received waveform attribute samples with a multidimensional higher-order nonzero-lag auto-cumulant of a known waveform to thereby classify the received waveform.
It is further an object of the disclosure to present a novel method for characterizing a received waveform. The method includes sampling the received waveform to obtain a predetermined number of samples of an attribute of the received waveform; estimating a multidimensional higher-order nonzero-lag auto-cumulant of the received waveform attribute samples; and comparing the estimated auto-cumulant of the received waveform attribute samples with a multidimensional higher-order nonzero-lag auto-cumulant of a known waveform to thereby characterize the received waveform.
It is another object of the disclosure to present a novel method for identifying a received waveform. The method includes sampling the received waveform to obtain a predetermined number of samples of an attribute of the received waveform; estimating a multidimensional higher-order nonzero-lag auto-cumulant of the received waveform attribute samples; and comparing the estimated auto-cumulant of the received waveform attribute samples with a multidimensional higher-order nonzero-lag auto-cumulant of a known waveform to thereby identify the received waveform.
Additional features and advantages of the invention will be described hereinafter which form the subject of the claims of the invention. It should be appreciated by those skilled in the art that the conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present invention. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the invention as set forth in the appended claims. The novel features which are believed to be characteristic of the invention, both as to its organization and method of operation, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present invention.